Monotreatment With Conventional Antirheumatic Drugs or Glucocorticoids in Rheumatoid Arthritis

The meta-analysis investigates the association of conventional antirheumatic drugs and glucocorticoids with clinical outcomes among patients with rheumatoid arthritis.

This supplemental material has been provided by the authors to give readers additional information about their work.eMethods 1. Eligibility Criteria 1.1 Inclusion criteria • Types of studies: Open, single blind or double blind randomized controlled trials of RA patients published in English.• Types of participants: Patients above 15 years with RA according to the criteria defined by the authors of the article.• Types of interventions: Any csDMARD, unless deregistered due to toxicity, glucocorticoid, placebo or comparator without anti-inflammatory effect, which could be 1) no treatment, 2) pain-relieving treatment including non-steroid anti-inflammatory drugs, or 3) very-low dose of the investigated DMARD.Chloroquine and hydroxychloroquine were merged into one node.No other DMARDs or GC were merged into the same node, but placebo and comparators without systemic anti-inflammatory effects were intended to be merged into the placebo node unless a sensitivity analysis would show different effects between them.Concomitant treatment with drugs with no anti-inflammatory effects are allowed, even if the drugs are not balanced between the intervention group and the comparator group, provided that the drug does not interact with the tested DMARD.Concomitant GC treatment in DMARD studies, which existed at inclusion and was not changed during the study, was allowed.• Types of outcome measures: Relevant studies had to report outcomes on at least one of the following variables: TJC, SJC, ESR and CRP.• Duration of intervention: In relevant studies, the duration of the intervention had to be at least 12 weeks for DMARDs and at least 2 weeks for GC in the randomized phase.

Exclusion criteria
• Concomitant DMARD treatment in an intervention group randomized to a csDMARD or GC treatment (combination therapy) • Enriched design, defined as studies where participants were known responders or non-responders to one of the study drugs.
eMethods 2. Information Sources and Search Electronic sources include MEDLINE, Cochrane Central, Embase and Clinicaltrials.gov.The search was conducted on 13th of June 2018 and updated on the 12 th of January 2022 and restricted to published peerreviewed full-length articles in English but not restricted by any time constraints.We intended to search all RCTs of RA on the effect of any DMARD, GC and combinations of these and from this pool of RCTs to select those dealing with mono DMARD treatment and mono-GC treatment.The electronic search string can be found in the present appendix, section 3. Clinical trials.govwas searched for completed interventional studies of rheumatoid arthritis with results.Finally, we hand searched reference lists of included studies and previous meta-analyses.We conducted a post-hoc screening in PubMed on September 15 th , 2022. the level of full article assessment according to a predefined procedure according to which studies were excluded with a reason or included and categorized according to the type of intervention.The reasons for exclusion could be 1) the study could not with certainty be classified as an RCT 2) no separate RA-data were reported 3) the intervention could not be classified as a DMARD or glucocorticoid 4) none of the predefined outcomes were measured 5) the study was an extension study in which randomization had been repealed, and/or intervention changes allowed 6) the drug-design was enriched, i.e., one of the drugs to which the patients were randomized was already known to be effective or ineffective in that group of patients The included studies were categorized according to the type of intervention in the following groups: 1) mono-csDMARD treatment including mono-GC studies 2) combination-csDMARD treatment including combinations with GC 3) TNF inhibitor studies 4) IL-6 inhibitor studies 5) B-cell inhibitor studies 6) T-cell inhibitor studies 7) JAK-inhibitor studies 8) other, which could be new interventions appearing during the procedure or rare interventions not generally used.

Search string (("antirheumatic agents"[
Two authors pairwise (LSG/NG and GJ/SEA) independently and blinded to each other´s decisions selected studies.Any disagreements were solved by agreement between the two authors.Remaining disagreements were solved at a final meeting between all 4 authors.Data were recorded on a standardized extraction form using Excel spreadsheet software from Microsoft.Data were collected by a single author (LSG).All data were independently controlled by two authors (NKGL and NG).Any disagreements were solved by discussion between LSG and NG.eMethods 4. Data Items Baseline mean values (SD) and post treatment mean values (SD) of outcomes TJC, SJC, ESR and CRP were recorded.In addition, the following baseline variables for each treatment arm were recorded: mean age, female percentage, percent rheumatoid factor positivity, and time since diagnosis.It was the intention also to extract disease activity score (DAS) and health assessment questionnaire score (HAQ).We also recorded whether GC was allowed during the study and the GC dose, and whether the patients previously had been exposed to DMARDs and the adequacy of a previous DMARD response.

Estimation of missing SD values
If SD was not reported it was estimated from an SE, a CI, a p-value, a t-value, a 25-75% percentile range divided by1,35 as suggested in the Cochrane handbook (1), or from a full range multiplied by conversion factor from table 1 by Walther and Yao (2).If SD was reported for the baseline value, but not for the post-treatment value and the post-treatment value could not be estimated as described above, we imputed the SD/mean quotient for the baseline value and multiplied this with the post-treatment value to obtain an estimate of the posttreatment SD.
If an sd for one intervention group could be estimated by means of a specified significant p-value, but not for another intervention group because it was reported as "NS" (non-significant), we used the estimated sd from the significant group in the non-significant group.If an sd estimated by means of a conversion factor gave a significant result for the individual study, which was in contrast to a reported non-significant result, we relatively and pairwise imputed the sd to a level resulting in p > 0.05.Finally, we imputed the average of SD/mean for each study outcome and used this value as a conversion factor to calculate unreported SDs which could not be estimated by one of the mentioned methods.For SJC, we calculated this conversion factor to be 0,51 for baseline values and 0,82 for post treatment values, for TJC be 0,52 for baseline values and 0,77 for post treatment values, for ESR to be 0,53 for baseline values and 0,66 for post treatment values, and for CRP to be 0,89 for baseline values and 1,10 for post treatment values.

Conversion of median duration to mean duration
If time of duration of RA was reported for both time since diagnosis and time since debut of symptoms, the time since diagnosis was recorded.We recorded mean duration.Some studies reported both mean and median duration.We used these studies to estimate conversion factors, which we could use to translate median duration to mean duration in studies, which only reported median duration Six studies presented both mean and median duration in 9 treatment groups.In one study with an upper duration limit of 5 years at inclusion (3) there were only small differences in two treatment arms between mean duration (35 and 28 months) and median duration (34 and 31 months).Thus, in studies with a maximum duration of 5 years at inclusion, we translated median duration directly to mean duration (conversion factor 1).In one study with an upper duration limit of 10 years at inclusion (4) there were differences in 3 treatment arms between mean duration (21.6, 19.2 and 22.8 months) and median duration (12, 12 and 12 months).The mean ratio of 21.6/12, 19.2/12 and 22.8/12 was 1,75.Thus, in studies with a maximum duration of 5-10 years at inclusion, we translated median duration to mean duration by a conversion factor of 1,75.In three studies with no upper limit of disease duration at inclusion and a median disease duration of 36 months or less (5-7) there were differences in 7 treatment arms between mean duration (25.9, 21.9, 72,5, 68,2, 64, 55 and 75 months) and median duration (11.5, 11.2, 35.0, 36.0,36.0,24.0 and 36.0 months).The mean ratio of 25.9/11.5,21.9/11.2,72.5/35, 68.2/36, 64/36, 55/24 and 75/36 was 2.04.Thus, in studies with no maximum duration at inclusion and a median duration of 3 years or less, we translated median duration to mean duration by a conversion factor of 2.0.In one study with no upper limit of disease duration at inclusion and a median disease duration of 72 months (8) there were differences in 2 treatment arms between mean duration (8.3 and 8.5 years) and median duration (6,0 and 6,5 years).The mean ratio of 8,3/6 and 8,5/6,5 was 1,35.Thus, in studies with no maximum duration and a median duration of at least 6 years, we translated median duration to mean duration by a conversion factor of 1, 35.In studies with no maximum duration and a median duration between 3 and 6 years we interpolated conversion factors as shown in the below eTable A. Four studies did not give information about duration.One study (9) reported that 68% in one arm and 87% in the other arm had joint erosions and based on this information the mean duration was estimated to be 3 years (36 months) and 5 years (60 months) (10).One study (11) reported that 67% in one arm and 61% in the other arm had joint erosions and based on this information the mean duration was estimated to be 3 years (36 months) and 2,5 years (30 months) (10).In one study (12), all patients had erosions and the mean duration was accordingly estimated to be 7 years (84 months) (10).

eTable A: Conversion factor of median to mean duration
One study (13) reported that only 25% of patients had been on DMARDS and 15% of patients had been on GC before inclusion in the study.We therefore estimated that the mean disease duration was relatively short and imputed a value of 12 months.

Conversion of median age to mean age
Two studies reported both mean and median age.The difference was small, varying between 0 and 2 years in four treatment arms.Consequently, we used the median age as mean age, when only the median age was reported.

Conversion of Ritchie Index to tender joint count (TJC).
In case that the total number of joints included in the calculation of the Ritchie index was not reported we assumed that it was based on evaluation of 53 joints as in the original definition of the Ritchie index.
Considering that the Ritchie index consists of 4 evaluations of 0, 1, 2, and 3, we assumed that the mean Ritchie of an evaluated joint would be 1.5 ((0+1+2+3)/4).In 14 studies, which evaluated both Ritchie index and TJC, the relation between TJC and Ritchie was as shown in eTable B, confirming that a conversion factor of 1.5 was suitable.In addition, we used the terms "mean erythrocyte sedimentation rate and median erythrocyte sedimentation and rheumatoid arthritis", which revealed 103 articles in PubMed.We identified 2 studies (18,19), which showed data on both mean and median ESR from 3 populations (eTable D).

eTable B: Relationship between TJC and Ritchie index in
Consequently, we used the mean weighted value of 1. 23  5.9 Conversion of median CRP to mean CRP We used the terms "mean CRP and median CRP and rheumatoid arthritis", which revealed 88 articles in PubMed.We identified 3 references (18,20,21, which reported data on both mean and median CRP from 5 study populations (eTable E).
Consequently, we used the weighted mean of 2.16 from these 5 populations as a conversion factor of median CRP to mean CRP (eTable E).

Estimation of number of joints counted
The classical joint evaluation includes 68 joints for TJC and 66 joints for SJC.If the number of evaluated joints was not reported, we assumed this to be 68 for TJC and 66 for SJC.

Estimation of DAS28
DAS28 is used both as a regression factor in meta regression and as an efficacy measure.As DAS28 is rarely reported directly, we had to estimate DAS28 form other efficacy measures.Therefore, we had to also estimate these efficacy measures in case they were not reported.
5.12 Estimation of missing baseline SJC, TJC, ESR and CRP for calculation of DAS28.
In studies which reported the effect but did not report the baseline values, we estimated the baseline values.For TJC and SJC we calculated the weighted mean of values in the following categories of baseline total joint count:  (23).We used this sd value for all studies.We divided this sd by sqrt n for each study and thus obtained a se value for each study.

Estimation of missing SJC values for DAS28
We used TJC values as a direct substitute for missing SJC values (36 studies)

Estimation of missing ESR values for DAS28
ESR was available in 116 studies.We calculated the ESR/CRP ratio for each value and then calculated the mean to be 1,37, which was used as a conversion factor of CRP to ESR in 5 studies.If CRP was not available for estimation of a missing ESR, we used the TJC conversion factor (2,56) to estimate ESR in 11 studies.
5.16 Conversion of SJC 29-68 to SJC 28 and of TJC 29-68 to TJC 28 Fuchs and Pincus (24) investigated systematically the individual participant data from three randomized trials, which published SJC based on 28 and 66 joints and found the percentage of SJC28/SJC66 to be 68%, 69%, and 83% (mean value 73%).We chose to use the mean value (73%) as a conversion factor between SJC28 and SJC66 and constructed a nomogram to translate SJC values between 28 and 66 to SJC 28 (eTable F).
Similarly, Fuchs and Pincus also investigated TJC and found the percentage of TJC28/TJC66 to be 57%, 60%, and 71% (mean value 63%).We chose to use this mean value (63%) as a conversion factor between TJC28 and TJC66 and constructed a nomogram to translate TJC values between 28 and 66 to TJC 28 (eTable F).We estimated mean DAS28 values from mean TJC, SJC, ESR and CRP values at baseline and after treatment, but as it was impossible reliably to estimate S.D. values, we could not estimate a DAS28 effect weighted by both S.D. and number of participants.However, we used a recently calculated mean S.D. of 130 studies, which was 1.1 (22), and successively calculated S.E. for each study by dividing with the square root of the number of participants.We then calculated a DAS28 effect, which consequently was weighted by number of participants.

Estimation of use Glucocorticoids (GC)
Most studies reported whether the use of GC was allowed or not.We recorded this information as a yes/no recording.In studies which did not mention that GC was allowed during the trial, we assumed that GC was not allowed (25)(26)(27)(28).Some studies mentioned that patients using GC were excluded.We assumed that GC was not allowed in these studies during the trial (29)(30)(31)(32)(33)(34) We also estimated the dose of GC in the treatment arms, but due to insufficient information in many studies this recording is less reliable.For all studies, which reported the number of patients receiving GC in each treatment arm, we calculated the mean fraction of patients to receive GC per treatment arm to be 36%.We used this percentage to estimate the number of patients receiving GC in studies reporting GC to be allowed, but not reporting the fraction of patients receiving GC.For all studies, which reported the doses of GC given to the patients receiving GC, we calculated the mean dose to be 5.64 mg per day per GC treated patient.We used this dose to estimate the dose in studies not reporting the GC dose.
The mean dose per treatment arm including patients not being treated with GC in studies which reported these doses sufficiently was 2,10 mg.The estimated mean dose of all treatment arms based on the premises mentioned above was 2.11 mg.

Transformation of pl-viscosity to ESR
Viscosity vsl.ESR can be transformed from pl-viscosity by the following formula: y = 107,08x -162,47, where y = ESR in mm at one hour and x = PV in centipoise at 25°C (35).

Transformation of Lansbury index to TJC and SJC
The Lansbury articular index is an index of total possible inflammation by adding the scores for the severity of inflammation of each joint, weighted for joint surface area.It is based on evaluation of all joints including sternoclavicular joints.We identified two studies (36,37) (38).According to this system, bias is estimated by means of 5 domains: 1, bias arising from the randomization process; 2, bias due to deviations from intended interventions; 3, bias due to missing outcome data; 4, bias in measurement of the outcome; and 5, bias in selection of the reported result.We only evaluated the first 3 domains.We did not evaluate domain 4 because we did our bias assessment on study level instead of outcome level, and domain 5 because most of these older studies were from a time where publication of pre-study plans was not common practice.Each of the domains is classified into three levels ("low risk", "some concern", "high risk").We intended to assign each level with a number, 0 for "low risk", 1 for "some concern" and 2 for "high risk".For each study these numbers were intended to be added to a sum representing the total risk of bias to be used as a potential effect modifier in a meta-regression analysis to explore the effect of bias on the outcome.The bias assessments of all included studies were performed by a single author (LSG).The assessments were controlled by 3 authors (SEA, GJ and NG), who each controlled 1/3 of the bias assessments.
eMethods 7. Planned Method of Analysis We chose noninformative priors, with a normal distribution with a mean of zero and a precision of 0.0001 for treatment effects, trial baselines, and regression parameters, and a uniform distribution between 0 and 5 for between trial S.D.. We investigated fitting both random and fixed effects models.Models were run in WinBUGS (39) for 20.000 burn-in iterations followed by 30.000 monitored iterations with a thin of 10.Convergence was assessed using traceplots and Brooks-Gelman-Rubin (BGR) plots (40).Model fit was assessed using the deviance information criterion (DIC), the effective number of parameters (pD) and the posterior residual deviance.A more complex model was considered more parsimonious (and therefore selected) if the DIC was 3-5 points lower than a simpler model.

Assessment of Inconsistency:
For the assessment of inconsistency, we fitted an unrelated mean effects model (UME) and compared this to the consistency NMA model.For each treatment comparison this allowed comparison between NMA consistency estimates and UME direct estimates, using the posterior median and 95% credible intervals (95%CrI).Disagreements were indicative of potential inconsistency, defined as comparisons in which the 95%CrIs did not overlap.We also plotted a dev-dev plot comparing the residual deviances for each data point in the UME compared to those in the NMA.If the residual deviance in the NMA was 0.5 or more than in the UME (41), we reviewed the study report for an explanation and reasons to exclude the study.eMethods 8. Effect Modifier Analysis 1.8.1 Effect modifier regression analyses.When assessing whether a regression parameter was likely to result in meaningful effect modification, we looked at the DIC, the total residual deviance compared to the number of arms, and the pD.We compared these model fit statistics with those from the unadjusted model.

Subgroup analyses.
For each relevant variable, we split the dataset into subgroups, and compared each individual measure of relative treatment effect to the one found in the unadjusted random-effects model.We performed subgroup analyses of the following categorical variables: 1) Whether or not glucocorticoids were allowed in the study as addition to the study drug.
2) Previous DMARD treatment, with 3 possible categories: a) treatment-naïve patients b) patients could have received DMARDS before, but the outcome was not used as inclusion/exclusion criteria c) included patients were known DMARD non-responders or had stopped the DMARD treatment due to adverse events 3) Analysis type, with two possible categories: a) intention to treat analysis b) completer analysis eResults 1. Study Selection For the present analysis, we selected 225 reports classified as mono csDMARD (Figure 1: 1098 identified RCTs minus 873 RCTs including other interventions than mono csDMARD treatment) and went through a second level of categorization in which 143 studies were included and 82 excluded.Reasons for exclusion during the search of the 225 identified mono-DMARD studies were the same as during the primary search.In addition, we excluded studies in which relevant outcome data were reported, but presented in a way, which made it impossible to transform them to be integrable in the meta-analysis, and studies in which the treatment groups were randomized to drug tapering or different drug doses without control group.We excluded 82 studies with reasons (Figure 1) and thus included 143 studies by the electronic search.The search in clinicaltrials.govresulted in 502 references.These dealt with biological drugs, target specific drugs, combination drug treatments, non-DMARD interventions, interventions for comorbidities (diabetes, hypercholesterolemia, gastric ulcer) and analgesics.Some studies were not RCTs, but dose-studies, pharmacokinetic studies, device studies or cohort studies.We found 4 mono-GC studies, one which investigated ultrasound changes and DAS28 (without details on TJC, SJC, ESR or CRP), and 3 slow-release GC studies, which investigated the effect on morning stiffness.
Additionally, we found two studies investigating Tripterygium Wilfordi Hook, which both were identified by our electronic search (42)(43).Accordingly, clinicaltrials.govdid not supply additional studies to be included in the NMA.Additional 26 studies were identified by hand search in reference lists of included studies and metaanalyses.Of these 169 studies (143 + 26), 38 were parallel publications.Thus, we included 131 publications (appendix reference list eR 1-131), which published 133 interventions.One study was included, but the two interventions were not connected to the network (44).The remaining 132 interventions (130 references) were included in the NMA.In our post-hoc screening search we used the term "rheumatoid arthritis" arthritis (6824 hits) filtered by the term "randomized controlled trial" (130 hits).Among the 130 studies we identified 5 drug interventions not involving targeted drugs.Two included non csDMARDs (n-acetyl cysteine and curcumin), one was a combination drug study, one a methotrexate dose-escalation study and one an add-on GC study.Thus, none of these studies were eligible.Consequently, our search is up to date on September 15th, 2022.
eResults 2. Primary Outcome (Complete Analysis) With Methotrexate as Reference, Unadjusted Analysis The most frequently evaluated outcome was tender joints (118 studies) either as a number (TJC) or as a Ritchie index, which was converted to a number (TJC) as explained in section 4. Thus, to obtain a complete analysis we imputed 14 missing TJC values based on available SJC or ESR values as explained in eMethods 5 In the unadjusted analysis the difference between the FE and RE model was small.Sulfasalazine, mycophenolate, and lobenzarit changed from being outside the credible interval (FE) to be marginally inside (RE) (eTable 2).An RE sensitivity analysis of 118 studies (28 treatments) without imputed TJC values showed comparable results to the complete unadjusted analysis regarding the point estimates.The position in the credible interval was unchanged for all drugs (eTable 3).
eResults 3. Effect Modifier Analyses Mean baseline DAS28 with imputed values (n=132) was a significant effect modifier.Also, the model fit parameters improved in the DAS28 model with imputed values.Baseline das28 mean is correlated to the treatment effect.A 1-unit increase in baseline mean DAS28 increases the treatment effect by 2.16 (95%CrI: 0.74, 3.45).The network meta-regression model adjusting for mean baseline DAS28 was therefore our selected model.Network meta-regression models including other covariates did not improve model fit significantly (eTable 4).
The subgroup analyses of the categorical variables showed TJCi 95% credible interval overlap in 10 treatment groups with previous inadequate response to a csDMARD, unspecified use of csDMARDs and no use of csDMARDs, and a non-overlapping interval in only one group (placebo) with a previous inadequate response to a csDMARD (eFigure 1A).There was overlap of TJCi CrIs in 12 treatment groups estimated according to statistical method of ITT or completer (eFigure 1B).Finally, there was overlap of TJCi CrIs in 11 treatment groups in which GC was allowed or not allowed (eFigure 1C).

Risk of bias influence on outcome
Most studies had a high risk of bias.We ran separate NMAs on only 6 studies which had no domains with high risk of bias, but these were not meaningful due to the large lack of interventions.Instead, we assessed the association between bias and estimated effects.We performed a meta-regression analysis where the assigned risk of bias sum was included as a potential effect modifier.This analysis had no impact on the outcome (eTable 4) eResults 4. Primary Outcome With Methotrexate as Reference, Adjusted for Baseline Disease Activity Score 28 (DAS28).First, we explored potential effect modifiers in meta-regression analyses of TJC outcome (n = 132) versus the potential effect modifier.Some effect modifiers were not reported in all studies.Hence the number of studies and the number of arms was smaller than the complete dataset.In that case, we ran a separate unadjusted model based on only the subset of studies, that reported the regression factor, and compared the model fit statistics between the adjusted and unadjusted model for the same dataset.The following effect modifiers were analyzed one by one: Study year (n=132), study duration (n=132), duration of RA (n=131), mean age (n=126), female% (n=121), RF positivity (n=73), bias estimation (n=132), DAS28 with (n=132) and without imputed values (n=77).None of the effect modifiers without imputations were significantly associated with TJC (eTable 4).eResults 5. Exploration of Inconsistency Inconsistency was explored by means of an unrelated mean effects model (UME), which was compared to the full model with DAS as a regression factor.An UME model is a model which only uses direct comparisons.If the results from the UME model differ significantly from the NMA model, it suggests there is a problem with inconsistency, that is, the direct and indirect evidence is not in accordance.We looked at both the differences between all treatments, i.e., 406 comparisons (the sum of 28 to1) calculated for both the full model and the UME model and a dev-dev plot of the 132 trials.There were no major disagreements between the two models.When comparing the relative treatment effects between individual treatment principles compared in pairs, there was an overlap between the UME and the NMA model in all cases.This suggests they do not differ significantly from each other, hence there is no sign of inconsistency between the direct and indirect model.eFigure 4 shows the dev-dev plot.This is a comparison of how much each comparison contributes to the total residual deviance.If a certain comparison contributes much more to the total residual deviance in the NMA model compared to the UME model, it suggests inconsistency between the direct and indirect evidence.Trials, where the deviance in the full model was 0.5 or bigger than in the UME model, was checked for any reason for inconsistency.In the present model of 132 trials, the dev-dev plot revealed 11 trials, which had to be checked for inconsistency, but no obvious reason for inconsistency was identified.Model fit of primary analysis: In the NMA model, the total residual deviance is 276.0 (275 study arms), DIC is 1304.03,between-study S.D. mean is 0.8548 with median 0.8703 and 95%CrI 0.05991 to 1.574.In the UME model, the total residual deviance is 274.5, the DIC is 1327.040,and the between-study S.D. is 0.8066 (median and 95% CrI is 0.7949 (0.06692; 1.622)).eResults 6. Results of Additional Analyses Separation of chloroquine and hydroxychloroquine: There were 9 arms for Chloroquine (Cl) and 10 arms for hydroxychloroquine (HCl).There was a large overlap between the credible intervals intervals (CrL) for the two treatments.Neither of the two treatment regimens had CrL that overlapped with placebo (Pl) -3.615 (-5.954;-1.279)forChloroquine as the reference and -2.367 (-0.9759 ;-3.81 ) for hydroxychloroquine as the reference.When Chloroquine is used as the reference, the relative effect estimate for hydroxychloroquine is -1.244 ( -3.852;1.38).2) Walter SD, Yao X.Effect sizes can be calculated for studies reporting ranges for outcome variables in systematic reviews.J Clin Epidemiol.2007; 60:849-52.Comparison between individual treatment principles in the NMA with DAS28 regression.Median, 2,5% fractile, 97,5 % fractile.Rows used as reference., so a negative value with a 95% CrI not including zero means the treatment principle in the row is significantly superior to the one in the column, while a positive value means the treatment principle in the row is significantly inferior to the one in the column.
LS et al.JAMA Network Open.
LS et al.JAMA Network Open.
LS et al.JAMA Network Open.

eFigure 4 .
dev-dev Plot of Unrelated Mean Effects Mean vs Network Meta-Analyses MeanPlot showing mean in unrelated mean effects model versus mean in model with mixed treatment effects.

eResults 5. Exploration of Inconsistency eResults 6. Results of Additional Analyses eReferences. eTable 1. Baseline
Characteristics, Primary Outcome and Bias Score of Included Studies Primary Outcome and Sensitivity Outcomes With Placebo as Reference eTable 6. Comparisons of Tender Joint Count Imputed (TJCi) Outcome of All Interventions vs All Interventions eTable 7. Model Fit Data for Primary Outcome and Sensitivity Outcomes eTable 8.Primary and Sensitivity Outcomes Calculated With Prior 0-20 eFigure 1. Subgroup Analyses eFigure 2. Forest Plot of Primary Tender Joint Count Imputed (TJCi) Outcomes vs Peroral Methotrexate eFigure 3. Rankograms of 29 Tested Interventions eFigure 4. dev-dev Plot of Unrelated Mean Effects Mean vs Network Meta-Analyses Mean

eTable G; Conversion factor of Lansbury articular index to TJC and SJC
, which measured both TJC, SJC and Lansbury articular index and based on 12 estimations we calculated conversion factors to convert Lansbury articular index to TJC and SJC based on evaluation of 28 joints, as shown in eTable G

eTables and eFigures eTable 1. Baseline Characteristics, Primary Outcome and Bias Score of Included Studies
JW, Bluhm GB, Duncan H, Sharp JT, Ensign DC, McCrum WR.Gold salts in the treatment of rheumatoid arthritis.A double-blind study.Ann Intern Med 1974;80(1):21-6.4) Capell HA, Madhok R, Porter DR, et al.Combination therapy with sulfasalazine and methotrexate is more effective than either drug alone in patients with rheumatoid arthritis with a suboptimal response to sulfasalazine: results from the double-blind placebo-controlled MASCOT study.Ann Rheum Dis.2007; 66:235-41.